Overview

Dataset statistics

Number of variables19
Number of observations846
Missing cells41
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory168.7 KiB
Average record size in memory204.2 B

Variable types

Numeric18
Categorical1

Warnings

circularity is highly correlated with max.length_rectangularity and 1 other fieldsHigh correlation
distance_circularity is highly correlated with scatter_ratio and 1 other fieldsHigh correlation
scatter_ratio is highly correlated with distance_circularity and 4 other fieldsHigh correlation
elongatedness is highly correlated with distance_circularity and 4 other fieldsHigh correlation
pr.axis_rectangularity is highly correlated with scatter_ratio and 3 other fieldsHigh correlation
max.length_rectangularity is highly correlated with circularityHigh correlation
scaled_variance is highly correlated with scatter_ratio and 3 other fieldsHigh correlation
scaled_variance.1 is highly correlated with scatter_ratio and 3 other fieldsHigh correlation
scaled_radius_of_gyration is highly correlated with circularityHigh correlation
skewness_about has 77 (9.1%) zeros Zeros
skewness_about.1 has 30 (3.5%) zeros Zeros

Reproduction

Analysis started2021-06-20 12:37:14.911002
Analysis finished2021-06-20 12:38:24.206811
Duration1 minute and 9.3 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

compactness
Real number (ℝ≥0)

Distinct44
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.678487
Minimum73
Maximum119
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:24.400091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile82
Q187
median93
Q3100
95-th percentile108
Maximum119
Range46
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.234474253
Coefficient of variation (CV)0.08790144373
Kurtosis-0.5352753539
Mean93.678487
Median Absolute Deviation (MAD)6
Skewness0.3812706326
Sum79252
Variance67.80656623
MonotocityNot monotonic
2021-06-20T18:08:24.582645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
8960
 
7.1%
8648
 
5.7%
9345
 
5.3%
8545
 
5.3%
9042
 
5.0%
9139
 
4.6%
9435
 
4.1%
8834
 
4.0%
9730
 
3.5%
9830
 
3.5%
Other values (34)438
51.8%
ValueCountFrequency (%)
731
 
0.1%
761
 
0.1%
772
 
0.2%
784
 
0.5%
795
 
0.6%
8012
1.4%
8113
1.5%
8219
2.2%
8317
2.0%
8420
2.4%
ValueCountFrequency (%)
1191
 
0.1%
1171
 
0.1%
1161
 
0.1%
1153
 
0.4%
1141
 
0.1%
1133
 
0.4%
1122
 
0.2%
1114
 
0.5%
1107
0.8%
10914
1.7%

circularity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)3.2%
Missing5
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean44.82877527
Minimum33
Maximum59
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:24.741688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile36
Q140
median44
Q349
95-th percentile55
Maximum59
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.152171862
Coefficient of variation (CV)0.1372371167
Kurtosis-0.9229247533
Mean44.82877527
Median Absolute Deviation (MAD)5
Skewness0.2618093968
Sum37701
Variance37.84921862
MonotocityNot monotonic
2021-06-20T18:08:24.901489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4359
 
7.0%
4558
 
6.9%
4450
 
5.9%
4648
 
5.7%
4247
 
5.6%
3847
 
5.6%
4042
 
5.0%
3942
 
5.0%
3742
 
5.0%
3641
 
4.8%
Other values (17)365
43.1%
ValueCountFrequency (%)
332
 
0.2%
349
 
1.1%
3516
 
1.9%
3641
4.8%
3742
5.0%
3847
5.6%
3942
5.0%
4042
5.0%
4135
4.1%
4247
5.6%
ValueCountFrequency (%)
591
 
0.1%
585
 
0.6%
5712
 
1.4%
5615
 
1.8%
5533
3.9%
5439
4.6%
5330
3.5%
5228
3.3%
5129
3.4%
5016
1.9%

distance_circularity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)7.5%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean82.11045131
Minimum40
Maximum112
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:25.072441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile58
Q170
median80
Q398
95-th percentile107
Maximum112
Range72
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.77829181
Coefficient of variation (CV)0.1921593604
Kurtosis-0.9772870801
Mean82.11045131
Median Absolute Deviation (MAD)12
Skewness0.1065848586
Sum69137
Variance248.9544923
MonotocityNot monotonic
2021-06-20T18:08:25.250324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6646
 
5.4%
7041
 
4.8%
6829
 
3.4%
10328
 
3.3%
8527
 
3.2%
10127
 
3.2%
10526
 
3.1%
7725
 
3.0%
7225
 
3.0%
9825
 
3.0%
Other values (53)543
64.2%
ValueCountFrequency (%)
401
 
0.1%
421
 
0.1%
441
 
0.1%
471
 
0.1%
491
 
0.1%
502
 
0.2%
516
0.7%
522
 
0.2%
539
1.1%
545
0.6%
ValueCountFrequency (%)
1121
 
0.1%
1104
 
0.5%
10915
1.8%
10817
2.0%
10713
1.5%
10612
1.4%
10526
3.1%
10421
2.5%
10328
3.3%
1026
 
0.7%

radius_ratio
Real number (ℝ≥0)

Distinct134
Distinct (%)16.0%
Missing6
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean168.8880952
Minimum104
Maximum333
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:25.442011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile120
Q1141
median167
Q3195
95-th percentile222
Maximum333
Range229
Interquartile range (IQR)54

Descriptive statistics

Standard deviation33.52019797
Coefficient of variation (CV)0.1984757891
Kurtosis0.3049212019
Mean168.8880952
Median Absolute Deviation (MAD)27
Skewness0.3949776679
Sum141866
Variance1123.603672
MonotocityNot monotonic
2021-06-20T18:08:25.620553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19717
 
2.0%
16215
 
1.8%
16913
 
1.5%
12513
 
1.5%
13912
 
1.4%
19912
 
1.4%
18612
 
1.4%
13012
 
1.4%
15012
 
1.4%
13611
 
1.3%
Other values (124)711
84.0%
ValueCountFrequency (%)
1041
 
0.1%
1051
 
0.1%
1091
 
0.1%
1103
0.4%
1114
0.5%
1121
 
0.1%
1134
0.5%
1144
0.5%
1154
0.5%
1167
0.8%
ValueCountFrequency (%)
3331
0.1%
3221
0.1%
3061
0.1%
2521
0.1%
2501
0.1%
2462
0.2%
2381
0.1%
2351
0.1%
2342
0.2%
2321
0.1%

pr.axis_aspect_ratio
Real number (ℝ≥0)

Distinct37
Distinct (%)4.4%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean61.67890995
Minimum47
Maximum138
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:25.790699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile53
Q157
median61
Q365
95-th percentile71
Maximum138
Range91
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.891463066
Coefficient of variation (CV)0.1279442693
Kurtosis29.88913462
Mean61.67890995
Median Absolute Deviation (MAD)4
Skewness3.830362075
Sum52057
Variance62.27518932
MonotocityNot monotonic
2021-06-20T18:08:25.954124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
6469
 
8.2%
5964
 
7.6%
6258
 
6.9%
5657
 
6.7%
6046
 
5.4%
6345
 
5.3%
5744
 
5.2%
5843
 
5.1%
6142
 
5.0%
6538
 
4.5%
Other values (27)338
40.0%
ValueCountFrequency (%)
472
 
0.2%
484
 
0.5%
493
 
0.4%
505
 
0.6%
5111
 
1.3%
5214
 
1.7%
5327
3.2%
5438
4.5%
5537
4.4%
5657
6.7%
ValueCountFrequency (%)
1381
 
0.1%
1331
 
0.1%
1262
 
0.2%
1051
 
0.1%
1031
 
0.1%
1021
 
0.1%
971
 
0.1%
761
 
0.1%
755
0.6%
749
1.1%

max.length_aspect_ratio
Real number (ℝ≥0)

Distinct21
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.567375887
Minimum2
Maximum55
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:26.103246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median8
Q310
95-th percentile12
Maximum55
Range53
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.601216661
Coefficient of variation (CV)0.5370625407
Kurtosis58.37545547
Mean8.567375887
Median Absolute Deviation (MAD)2
Skewness6.778393619
Sum7248
Variance21.17119476
MonotocityNot monotonic
2021-06-20T18:08:26.253245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7168
19.9%
6132
15.6%
8113
13.4%
10112
13.2%
11108
12.8%
994
11.1%
551
 
6.0%
1230
 
3.5%
418
 
2.1%
34
 
0.5%
Other values (11)16
 
1.9%
ValueCountFrequency (%)
21
 
0.1%
34
 
0.5%
418
 
2.1%
551
 
6.0%
6132
15.6%
7168
19.9%
8113
13.4%
994
11.1%
10112
13.2%
11108
12.8%
ValueCountFrequency (%)
551
 
0.1%
522
0.2%
492
0.2%
481
 
0.1%
461
 
0.1%
431
 
0.1%
251
 
0.1%
222
0.2%
191
 
0.1%
133
0.4%

scatter_ratio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct131
Distinct (%)15.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean168.9017751
Minimum112
Maximum265
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:26.427224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile125.2
Q1147
median157
Q3198
95-th percentile222
Maximum265
Range153
Interquartile range (IQR)51

Descriptive statistics

Standard deviation33.21484792
Coefficient of variation (CV)0.1966518581
Kurtosis-0.6165699869
Mean168.9017751
Median Absolute Deviation (MAD)20
Skewness0.607270584
Sum142722
Variance1103.226122
MonotocityNot monotonic
2021-06-20T18:08:26.606173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15035
 
4.1%
15129
 
3.4%
14929
 
3.4%
15721
 
2.5%
15219
 
2.2%
14818
 
2.1%
15317
 
2.0%
15516
 
1.9%
14714
 
1.7%
15414
 
1.7%
Other values (121)633
74.8%
ValueCountFrequency (%)
1121
 
0.1%
1144
0.5%
1152
 
0.2%
1163
 
0.4%
1172
 
0.2%
1185
0.6%
1196
0.7%
1203
 
0.4%
1211
 
0.1%
1228
0.9%
ValueCountFrequency (%)
2651
 
0.1%
2621
 
0.1%
2611
 
0.1%
2601
 
0.1%
2573
0.4%
2561
 
0.1%
2551
 
0.1%
2521
 
0.1%
2511
 
0.1%
2502
0.2%

elongatedness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)4.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.93372781
Minimum26
Maximum61
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:26.782292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile30
Q133
median43
Q346
95-th percentile54
Maximum61
Range35
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.816185719
Coefficient of variation (CV)0.1909473223
Kurtosis-0.8666008279
Mean40.93372781
Median Absolute Deviation (MAD)6
Skewness0.04784701482
Sum34589
Variance61.09275919
MonotocityNot monotonic
2021-06-20T18:08:26.944747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4573
 
8.6%
3173
 
8.6%
4472
 
8.5%
4360
 
7.1%
4659
 
7.0%
3050
 
5.9%
3244
 
5.2%
3328
 
3.3%
4227
 
3.2%
5025
 
3.0%
Other values (25)334
39.5%
ValueCountFrequency (%)
2610
 
1.2%
277
 
0.8%
287
 
0.8%
292
 
0.2%
3050
5.9%
3173
8.6%
3244
5.2%
3328
 
3.3%
3421
 
2.5%
3525
 
3.0%
ValueCountFrequency (%)
611
 
0.1%
594
 
0.5%
584
 
0.5%
5712
1.4%
566
 
0.7%
5510
1.2%
5410
1.2%
5310
1.2%
5220
2.4%
5118
2.1%

pr.axis_rectangularity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)1.5%
Missing3
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean20.58244365
Minimum17
Maximum29
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:27.095290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17.1
Q119
median20
Q323
95-th percentile25
Maximum29
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.592933061
Coefficient of variation (CV)0.1259779016
Kurtosis-0.3905083833
Mean20.58244365
Median Absolute Deviation (MAD)2
Skewness0.7708887331
Sum17351
Variance6.72330186
MonotocityNot monotonic
2021-06-20T18:08:27.229610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
19237
28.0%
18129
15.2%
20116
13.7%
2489
 
10.5%
2558
 
6.9%
2352
 
6.1%
2248
 
5.7%
2147
 
5.6%
1743
 
5.1%
269
 
1.1%
Other values (3)15
 
1.8%
ValueCountFrequency (%)
1743
 
5.1%
18129
15.2%
19237
28.0%
20116
13.7%
2147
 
5.6%
2248
 
5.7%
2352
 
6.1%
2489
 
10.5%
2558
 
6.9%
269
 
1.1%
ValueCountFrequency (%)
291
 
0.1%
289
 
1.1%
275
 
0.6%
269
 
1.1%
2558
6.9%
2489
10.5%
2352
6.1%
2248
5.7%
2147
5.6%
20116
13.7%

max.length_rectangularity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.998818
Minimum118
Maximum188
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:27.390669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile126.25
Q1137
median146
Q3159
95-th percentile173
Maximum188
Range70
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.51565157
Coefficient of variation (CV)0.09807951018
Kurtosis-0.7700982384
Mean147.998818
Median Absolute Deviation (MAD)11
Skewness0.2563591641
Sum125207
Variance210.7041406
MonotocityNot monotonic
2021-06-20T18:08:27.629232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14537
 
4.4%
14437
 
4.4%
14333
 
3.9%
14727
 
3.2%
14626
 
3.1%
13424
 
2.8%
14823
 
2.7%
13122
 
2.6%
14121
 
2.5%
15819
 
2.2%
Other values (56)577
68.2%
ValueCountFrequency (%)
1182
 
0.2%
1192
 
0.2%
1201
 
0.1%
1212
 
0.2%
1223
 
0.4%
1233
 
0.4%
1248
0.9%
12512
1.4%
12610
1.2%
12717
2.0%
ValueCountFrequency (%)
1881
 
0.1%
1861
 
0.1%
1822
 
0.2%
1802
 
0.2%
1791
 
0.1%
1785
0.6%
1776
0.7%
1763
 
0.4%
1758
0.9%
1747
0.8%

scaled_variance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct128
Distinct (%)15.2%
Missing3
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean188.6310795
Minimum130
Maximum320
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:27.851215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile144
Q1167
median179
Q3217
95-th percentile234
Maximum320
Range190
Interquartile range (IQR)50

Descriptive statistics

Standard deviation31.4110036
Coefficient of variation (CV)0.1665208283
Kurtosis0.1200714326
Mean188.6310795
Median Absolute Deviation (MAD)20
Skewness0.6515982489
Sum159016
Variance986.6511469
MonotocityNot monotonic
2021-06-20T18:08:28.030587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17034
 
4.0%
16929
 
3.4%
17324
 
2.8%
17520
 
2.4%
16818
 
2.1%
17417
 
2.0%
17217
 
2.0%
17117
 
2.0%
16616
 
1.9%
22616
 
1.9%
Other values (118)635
75.1%
ValueCountFrequency (%)
1301
 
0.1%
1311
 
0.1%
1321
 
0.1%
1341
 
0.1%
1356
0.7%
1362
 
0.2%
1376
0.7%
1383
0.4%
1394
0.5%
1404
0.5%
ValueCountFrequency (%)
3201
 
0.1%
2881
 
0.1%
2871
 
0.1%
2853
0.4%
2803
0.4%
2781
 
0.1%
2752
0.2%
2722
0.2%
2692
0.2%
2672
0.2%

scaled_variance.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct422
Distinct (%)50.0%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean439.4940758
Minimum184
Maximum1018
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:28.225107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum184
5-th percentile229
Q1318
median363.5
Q3587
95-th percentile728
Maximum1018
Range834
Interquartile range (IQR)269

Descriptive statistics

Standard deviation176.6669027
Coefficient of variation (CV)0.4019778932
Kurtosis-0.2038188741
Mean439.4940758
Median Absolute Deviation (MAD)94.5
Skewness0.842033854
Sum370933
Variance31211.19451
MonotocityNot monotonic
2021-06-20T18:08:28.398952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3278
 
0.9%
3338
 
0.9%
3308
 
0.9%
3257
 
0.8%
3227
 
0.8%
3677
 
0.8%
3417
 
0.8%
3317
 
0.8%
3547
 
0.8%
3327
 
0.8%
Other values (412)771
91.1%
ValueCountFrequency (%)
1841
0.1%
1911
0.1%
1921
0.1%
1931
0.1%
1941
0.1%
1951
0.1%
1962
0.2%
1971
0.1%
2001
0.1%
2032
0.2%
ValueCountFrequency (%)
10181
0.1%
9981
0.1%
9871
0.1%
9821
0.1%
9681
0.1%
9661
0.1%
9571
0.1%
9561
0.1%
9541
0.1%
9282
0.2%

scaled_radius_of_gyration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)16.9%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean174.7097156
Minimum109
Maximum268
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:28.573055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile125
Q1149
median173.5
Q3198
95-th percentile228.85
Maximum268
Range159
Interquartile range (IQR)49

Descriptive statistics

Standard deviation32.58480823
Coefficient of variation (CV)0.1865082781
Kurtosis-0.4963362797
Mean174.7097156
Median Absolute Deviation (MAD)24.5
Skewness0.2793173323
Sum147455
Variance1061.769728
MonotocityNot monotonic
2021-06-20T18:08:28.749051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18624
 
2.8%
17120
 
2.4%
17619
 
2.2%
17416
 
1.9%
18515
 
1.8%
17315
 
1.8%
21415
 
1.8%
17214
 
1.7%
17713
 
1.5%
13913
 
1.5%
Other values (133)680
80.4%
ValueCountFrequency (%)
1091
 
0.1%
1123
0.4%
1131
 
0.1%
1141
 
0.1%
1152
0.2%
1162
0.2%
1173
0.4%
1182
0.2%
1194
0.5%
1202
0.2%
ValueCountFrequency (%)
2681
 
0.1%
2641
 
0.1%
2621
 
0.1%
2613
0.4%
2601
 
0.1%
2571
 
0.1%
2551
 
0.1%
2531
 
0.1%
2502
0.2%
2491
 
0.1%

scaled_radius_of_gyration.1
Real number (ℝ≥0)

Distinct39
Distinct (%)4.6%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean72.44774347
Minimum59
Maximum135
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:28.928193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile63
Q167
median71.5
Q375
95-th percentile85
Maximum135
Range76
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.486190276
Coefficient of variation (CV)0.1033322767
Kurtosis11.45575924
Mean72.44774347
Median Absolute Deviation (MAD)4.5
Skewness2.083496486
Sum61001
Variance56.04304484
MonotocityNot monotonic
2021-06-20T18:08:29.095061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7275
 
8.9%
7168
 
8.0%
6753
 
6.3%
7453
 
6.3%
7052
 
6.1%
7346
 
5.4%
6944
 
5.2%
6640
 
4.7%
6839
 
4.6%
7538
 
4.5%
Other values (29)334
39.5%
ValueCountFrequency (%)
591
 
0.1%
602
 
0.2%
6111
 
1.3%
6218
 
2.1%
6324
2.8%
6438
4.5%
6531
3.7%
6640
4.7%
6753
6.3%
6839
4.6%
ValueCountFrequency (%)
1351
 
0.1%
1271
 
0.1%
1191
 
0.1%
1181
 
0.1%
991
 
0.1%
971
 
0.1%
911
 
0.1%
902
 
0.2%
891
 
0.1%
885
0.6%

skewness_about
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)2.7%
Missing6
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.364285714
Minimum0
Maximum22
Zeros77
Zeros (%)9.1%
Memory size6.7 KiB
2021-06-20T18:08:29.262835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile16
Maximum22
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.920649076
Coefficient of variation (CV)0.7731659604
Kurtosis0.09407657316
Mean6.364285714
Median Absolute Deviation (MAD)4
Skewness0.7765187098
Sum5346
Variance24.21278733
MonotocityNot monotonic
2021-06-20T18:08:29.408241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
181
 
9.6%
077
 
9.1%
571
 
8.4%
470
 
8.3%
665
 
7.7%
263
 
7.4%
760
 
7.1%
356
 
6.6%
847
 
5.6%
946
 
5.4%
Other values (13)204
24.1%
ValueCountFrequency (%)
077
9.1%
181
9.6%
263
7.4%
356
6.6%
470
8.3%
571
8.4%
665
7.7%
760
7.1%
847
5.6%
946
5.4%
ValueCountFrequency (%)
224
 
0.5%
215
 
0.6%
203
 
0.4%
194
 
0.5%
186
 
0.7%
1711
1.3%
1611
1.3%
1519
2.2%
1418
2.1%
1326
3.1%

skewness_about.1
Real number (ℝ≥0)

ZEROS

Distinct41
Distinct (%)4.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.60236686
Minimum0
Maximum41
Zeros30
Zeros (%)3.5%
Memory size6.7 KiB
2021-06-20T18:08:29.572982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q319
95-th percentile29
Maximum41
Range41
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.936081294
Coefficient of variation (CV)0.7090796031
Kurtosis-0.1447277377
Mean12.60236686
Median Absolute Deviation (MAD)6
Skewness0.6880171692
Sum10649
Variance79.85354889
MonotocityNot monotonic
2021-06-20T18:08:29.732129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1146
 
5.4%
744
 
5.2%
441
 
4.8%
940
 
4.7%
1438
 
4.5%
138
 
4.5%
237
 
4.4%
637
 
4.4%
536
 
4.3%
832
 
3.8%
Other values (31)456
53.9%
ValueCountFrequency (%)
030
3.5%
138
4.5%
237
4.4%
332
3.8%
441
4.8%
536
4.3%
637
4.4%
744
5.2%
832
3.8%
940
4.7%
ValueCountFrequency (%)
411
 
0.1%
401
 
0.1%
391
 
0.1%
386
0.7%
363
0.4%
354
0.5%
341
 
0.1%
335
0.6%
326
0.7%
315
0.6%

skewness_about.2
Real number (ℝ≥0)

Distinct30
Distinct (%)3.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean188.9195266
Minimum176
Maximum206
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:29.885019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum176
5-th percentile179.2
Q1184
median188
Q3193
95-th percentile200
Maximum206
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.155809475
Coefficient of variation (CV)0.03258429441
Kurtosis-0.5887584146
Mean188.9195266
Median Absolute Deviation (MAD)4
Skewness0.2493206901
Sum159637
Variance37.8939903
MonotocityNot monotonic
2021-06-20T18:08:30.035723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18862
 
7.3%
18760
 
7.1%
18960
 
7.1%
18653
 
6.3%
19247
 
5.6%
19042
 
5.0%
19141
 
4.8%
18339
 
4.6%
18039
 
4.6%
18136
 
4.3%
Other values (20)366
43.3%
ValueCountFrequency (%)
1763
 
0.4%
1775
 
0.6%
1784
 
0.5%
17931
3.7%
18039
4.6%
18136
4.3%
18226
3.1%
18339
4.6%
18432
3.8%
18534
4.0%
ValueCountFrequency (%)
2061
 
0.1%
2042
 
0.2%
2037
 
0.8%
2028
 
0.9%
20117
2.0%
20014
1.7%
19919
2.2%
19820
2.4%
19724
2.8%
19625
3.0%

hollows_ratio
Real number (ℝ≥0)

Distinct31
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.6323877
Minimum181
Maximum211
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2021-06-20T18:08:30.197679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum181
5-th percentile183
Q1190.25
median197
Q3201
95-th percentile207
Maximum211
Range30
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.438797429
Coefficient of variation (CV)0.03802436558
Kurtosis-0.8134350379
Mean195.6323877
Median Absolute Deviation (MAD)5
Skewness-0.2263412803
Sum165505
Variance55.33570719
MonotocityNot monotonic
2021-06-20T18:08:30.349605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
19853
 
6.3%
19651
 
6.0%
19951
 
6.0%
19751
 
6.0%
20146
 
5.4%
19543
 
5.1%
18340
 
4.7%
18438
 
4.5%
20038
 
4.5%
20235
 
4.1%
Other values (21)400
47.3%
ValueCountFrequency (%)
1812
 
0.2%
18225
3.0%
18340
4.7%
18438
4.5%
18526
3.1%
18616
 
1.9%
18719
2.2%
18815
 
1.8%
18916
 
1.9%
19015
 
1.8%
ValueCountFrequency (%)
2114
 
0.5%
2108
 
0.9%
20912
 
1.4%
20815
1.8%
20712
 
1.4%
20623
2.7%
20524
2.8%
20427
3.2%
20329
3.4%
20235
4.1%

class
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.7 KiB
car
429 
bus
218 
van
199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2538
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvan
2nd rowvan
3rd rowcar
4th rowvan
5th rowbus
ValueCountFrequency (%)
car429
50.7%
bus218
25.8%
van199
23.5%
2021-06-20T18:08:30.666380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-06-20T18:08:30.764198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
car429
50.7%
bus218
25.8%
van199
23.5%

Most occurring characters

ValueCountFrequency (%)
a628
24.7%
c429
16.9%
r429
16.9%
b218
 
8.6%
u218
 
8.6%
s218
 
8.6%
v199
 
7.8%
n199
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2538
100.0%

Most frequent character per category

ValueCountFrequency (%)
a628
24.7%
c429
16.9%
r429
16.9%
b218
 
8.6%
u218
 
8.6%
s218
 
8.6%
v199
 
7.8%
n199
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2538
100.0%

Most frequent character per script

ValueCountFrequency (%)
a628
24.7%
c429
16.9%
r429
16.9%
b218
 
8.6%
u218
 
8.6%
s218
 
8.6%
v199
 
7.8%
n199
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2538
100.0%

Most frequent character per block

ValueCountFrequency (%)
a628
24.7%
c429
16.9%
r429
16.9%
b218
 
8.6%
u218
 
8.6%
s218
 
8.6%
v199
 
7.8%
n199
 
7.8%

Interactions

2021-06-20T18:07:23.421949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:23.613407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:23.794773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:23.947188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.094140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.257193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.424208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.587391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.738316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:24.898108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.082110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.229371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.391397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.550790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.694399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.840232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:25.994737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.149753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.297297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.456694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.606333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.757496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:26.916092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:27.075464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:27.270441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:27.450456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:27.702015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:27.912441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:28.548275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:28.726068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:28.913533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.092670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.251768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.426491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.600559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.761178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:29.922043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:30.358231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:30.536371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:30.702546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:30.886886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:31.056521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:31.242808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:31.452843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:31.672680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:31.861196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.040571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.217991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.380848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.550712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.722899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:32.897967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.058086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.207595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.369500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.513280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.660410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.814984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:33.967253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.113347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.269704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.426539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.570603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.749238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:07:34.920219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-06-20T18:08:15.342234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:15.483799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:15.637594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:15.795963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:15.936470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:16.086935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:16.237518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:16.393002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:16.654596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:16.886270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.050490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.216387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.383170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.563667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.733469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:17.900197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.073459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.249310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.404572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.574411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.744364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:18.897459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.064091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.226025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.383868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.541623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.708087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:19.872466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.028006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.189531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.356171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.521481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.680639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:20.846723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.045792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.199425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.367417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.560779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.753442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-20T18:08:21.943175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-20T18:08:30.898449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-20T18:08:31.239582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-20T18:08:31.580684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-20T18:08:31.932671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-20T18:08:22.432377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-20T18:08:22.908744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-20T18:08:23.289612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-20T18:08:23.590916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

compactnesscircularitydistance_circularityradius_ratiopr.axis_aspect_ratiomax.length_aspect_ratioscatter_ratioelongatednesspr.axis_rectangularitymax.length_rectangularityscaled_variancescaled_variance.1scaled_radius_of_gyrationscaled_radius_of_gyration.1skewness_aboutskewness_about.1skewness_about.2hollows_ratioclass
09548.083.0178.072.010162.042.020.0159176.0379.0184.070.06.016.0187.0197van
19141.084.0141.057.09149.045.019.0143170.0330.0158.072.09.014.0189.0199van
210450.0106.0209.066.010207.032.023.0158223.0635.0220.073.014.09.0188.0196car
39341.082.0159.063.09144.046.019.0143160.0309.0127.063.06.010.0199.0207van
48544.070.0205.0103.052149.045.019.0144241.0325.0188.0127.09.011.0180.0183bus
5107NaN106.0172.050.06255.026.028.0169280.0957.0264.085.05.09.0181.0183bus
69743.073.0173.065.06153.042.019.0143176.0361.0172.066.013.01.0200.0204bus
79043.066.0157.065.09137.048.018.0146162.0281.0164.067.03.03.0193.0202van
88634.062.0140.061.07122.054.017.0127141.0223.0112.064.02.014.0200.0208van
99344.098.0NaN62.011183.036.022.0146202.0505.0152.064.04.014.0195.0204car

Last rows

compactnesscircularitydistance_circularityradius_ratiopr.axis_aspect_ratiomax.length_aspect_ratioscatter_ratioelongatednesspr.axis_rectangularitymax.length_rectangularityscaled_variancescaled_variance.1scaled_radius_of_gyrationscaled_radius_of_gyration.1skewness_aboutskewness_about.1skewness_about.2hollows_ratioclass
8368745.066.0139.058.08140.047.018.0148168.0294.0175.073.03.012.0188.0196van
8379446.077.0169.060.08158.042.020.0148181.0373.0181.067.012.02.0193.0199car
8389543.076.0142.057.010151.044.019.0149173.0339.0159.071.02.023.0187.0200van
8399044.072.0157.064.08137.048.018.0144159.0283.0171.065.09.04.0196.0203van
8409334.066.0140.056.07130.051.018.0120151.0251.0114.062.05.029.0201.0207car
8419339.087.0183.064.08169.040.020.0134200.0422.0149.072.07.025.0188.0195car
8428946.084.0163.066.011159.043.020.0159173.0368.0176.072.01.020.0186.0197van
84310654.0101.0222.067.012222.030.025.0173228.0721.0200.070.03.04.0187.0201car
8448636.078.0146.058.07135.050.018.0124155.0270.0148.066.00.025.0190.0195car
8458536.066.0123.055.05120.056.017.0128140.0212.0131.073.01.018.0186.0190van